The PL/Python language module automatically imports a Python module called plpy. The functions and constants in this module are available to you in the Python code as plpy.foo.
The plpy module provides two
plpy.execute with a query string and an
optional limit argument causes that query to be run and the
result to be returned in a result object. The result object
emulates a list or dictionary object. The result object can be
accessed by row number and column name. It has these additional
nrows which returns the
number of rows returned by the query, and
status which is the
SPI_execute() return value. The result object
can be modified.
rv = plpy.execute("SELECT * FROM my_table", 5)
returns up to 5 rows from my_table. If my_table has a column my_column, it would be accessed as:
foo = rv[i]["my_column"]
The second function,
plpy.prepare, prepares the execution plan for
a query. It is called with a query string and a list of
parameter types, if you have parameter references in the query.
plan = plpy.prepare("SELECT last_name FROM my_users WHERE first_name = $1", [ "text" ])
text is the type of the variable
you will be passing for $1. After
preparing a statement, you use the function
plpy.execute to run it:
rv = plpy.execute(plan, [ "name" ], 5)
The third argument is the limit and is optional.
Query parameters and result row fields are converted between PostgreSQL and Python data types as described in Section 42.3. The exception is that composite types are currently not supported: They will be rejected as query parameters and are converted to strings when appearing in a query result. As a workaround for the latter problem, the query can sometimes be rewritten so that the composite type result appears as a result row rather than as a field of the result row. Alternatively, the resulting string could be parsed apart by hand, but this approach is not recommended because it is not future-proof.
When you prepare a plan using the PL/Python module it is automatically saved. Read the SPI documentation (Chapter 43) for a description of what this means. In order to make effective use of this across function calls one needs to use one of the persistent storage dictionaries SD or GD (see Section 42.4). For example:
CREATE FUNCTION usesavedplan() RETURNS trigger AS $$ if SD.has_key("plan"): plan = SD["plan"] else: plan = plpy.prepare("SELECT 1") SD["plan"] = plan # rest of function $$ LANGUAGE plpythonu;
Functions accessing the database might encounter errors,
which will cause them to abort and raise an exception. Both
plpy.prepare can raise an instance of a
subclass of plpy.SPIError, which by
default will terminate the function. This error can be handled
just like any other Python exception, by using the try/except construct. For example:
CREATE FUNCTION try_adding_joe() RETURNS text AS $$ try: plpy.execute("INSERT INTO users(username) VALUES ('joe')") except plpy.SPIError: return "something went wrong" else: return "Joe added" $$ LANGUAGE plpythonu;
The actual class of the exception being raised corresponds to the specific condition that caused the error. Refer to Table A-1 for a list of possible conditions. The module plpy.spiexceptions defines an exception class for each PostgreSQL condition, deriving their names from the condition name. For instance, division_by_zero becomes DivisionByZero, unique_violation becomes UniqueViolation, fdw_error becomes FdwError, and so on. Each of these exception classes inherits from SPIError. This separation makes it easier to handle specific errors, for instance:
CREATE FUNCTION insert_fraction(numerator int, denominator int) RETURNS text AS $$ from plpy import spiexceptions try: plan = plpy.prepare("INSERT INTO fractions (frac) VALUES ($1 / $2)", ["int", "int"]) plpy.execute(plan, [numerator, denominator]) except spiexceptions.DivisionByZero: return "denominator cannot equal zero" except spiexceptions.UniqueViolation: return "already have that fraction" except plpy.SPIError, e: return "other error, SQLSTATE %s" % e.sqlstate else: return "fraction inserted" $$ LANGUAGE plpythonu;
Note that because all exceptions from the plpy.spiexceptions module inherit from SPIError, an except clause handling it will catch any database access error.
As an alternative way of handling different error conditions, you can catch the SPIError exception and determine the specific error condition inside the except block by looking at the sqlstate attribute of the exception object. This attribute is a string value containing the "SQLSTATE" error code. This approach provides approximately the same functionality
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